欧氏空间Rn的子空间的本性矩阵及其应用

提出欧氏空间Rn的子空间的本性矩阵的概念,并给出了在一类特征值反问题中的应用,证明了有s个已知互异特征值的实对称矩阵由其任何s-1个特征子空间唯一确定.     

用3个向量对构造梁振动系统的刚度矩阵

针对梁的离散化模型的刚度矩阵是五对角矩阵,梁振动反问题的实质是实对称五对角矩阵的特征值反问题.该文利用向量对、Moore-Penrose广义逆给出了实对称五对角矩阵向量对反问题存在唯一解的条件,并结合矩阵分块讨论了双对称五对角矩阵向量对反问题解存在唯一的条件,进而计算了次对角线位置元素为负,其它位置元素均为正的实对称五对角矩阵特征值反问题.由于构造梁的离散模型需要的数据可由测试得到,故而其结果适合于模态分析、系统结构的分析与设计等方面应用.最后给出了数值算例,通过数值讨论说明方法的有效性.     

用改进遗传算法求解矩阵实特征值

依据矩阵特征值的分布理论,通过确定矩阵实特征值的分布区域,用实数编码和具有自适应交叉概率和变异概率的遗传算法来求解矩阵实特征值的近似值.仿真结果表明,此算法可以达到一定的精度,具有一定的通用性.并给求矩阵特征值提供了一种快速的方法.     

低计算精度下实对称矩阵的特征值分解

正1引言1.1 背景简介设A ∈ R~(n×n)为n阶实对称矩阵,矩阵A的特征值分解是找正交矩阵U ∈R~(n×n),使得A=UAU~T,(1.1)其中U~T指U的转置,Λ为对角矩阵,且Λ=diag(λ_1,λ_2,…,λ_n),其中λ_i,i=1,…,n是矩阵A的特征值.矩阵A的奇异值分解为A=UEU~H,(1.2)其中,U ∈ C~(n×n)是酉矩阵,U~H是U的共轭转置,∑是非负实对角矩阵.当A正定时,奇异值分解和特征值分解等价.对一般实对称阵,奇异值和特征值绝对值相同.在实际应用中,往往不需要求得矩阵A的全部特征值和特征向量,只需要其绝对值最大的若干特征值所构成的近似特征值分解,以便进行矩阵近似求逆等任务.这种近似特征值分解被称为主特征值分解(Dominant Eigenvalue Decomposition),在矩阵近似求逆和主成分分析(PCA)[1]等方面有重要应用.     

特征值反问题的唯一性

证明了由特征值及特征向量反求矩阵时,特征值在对角矩阵中的排序可以是任意的,只须将对应特征向量作相应排序,所得矩阵唯一。对于重特征值的线性无关的特征向量可任意选取,所得矩阵唯一。     

p.n.p.矩阵的一些性质

一个n阶实方阵若其各阶主子式皆非正,则称为部分非正阵,简写作p.n.p.矩阵.特别地,各阶主子式皆负的p.n.p.矩阵称为部分负矩阵,简写为p.n.矩阵。文[1]、[5]讨论了p.n.p.矩阵的谱性质。本文在[5]的基础上讨论了p.n.p.矩阵的若干性质,并给出p.n.p.矩阵特征值的某些估计式。 引理1 设A=(A_(ij)_n×n为一p.n.p.矩阵,则A的特征值之实部不全为负(n≥2)。 证 设λ_1,λ_2,…,λ_n为A的全部特征值。假定A的每一特征值之实部皆为负。分两种情     

实对称五对角矩阵的两类广义特征值反问题

讨论了如下两类广义特征值反问题:(i)由给定的三个互异的特征对和给定的实对称正定五对角矩阵构造一个实对称五对角矩阵;(ii)由给定的三个互异特征对和给定的全对称正定五对角矩阵构造一个全对称五对角矩阵.利用线性方程组理论、对称向量和反对称向量的性质,分别得到了两类反问题存在唯一解的充要条件,并给出了解的表达式和数值算法;最后通过数值例子说明了算法的有效性.     

Hermitian Toeplitz矩阵向量乘积的快速算法（英文）

众所周知,大规模Hermitian Toeplitz矩阵向量乘积Ax可由快速Fourier变换(FFT)进行计算.事实上,Hermitian Toeplitz矩阵在酉相似变换下可约化为一个实的Toeplitz矩阵与Hankel矩阵之和.基于此,本文利用DCT和DST,构造了一个更有效的方法,只需O(n)的复运算.     

实对称区间矩阵特征值确界的交错定理及其应用

本文将实对称矩阵特征值的交错定理推广到实对称区间矩阵,给出了实对称区间矩阵特征值确界的交错定理,并应用该定理构造了估计实对称三对角区间矩阵特征值界的算法.文中数值例子表明,本文所给算法与一些现有算法相比在使用范围、计算精度和计算量等方面都具有一定的优越性.     

解不可压缩Navier-Stokes方程的非精确块因子分解预处理子

针对相关于不可压缩Navier-Stokes方程数值求解的一类3×3块结构的线性方程组,基于线性方程组的等价形式,构造了一个非精确的块因子分解预处理子,在新的特征值等价矩阵形式的基础上,得到了预处理矩阵特征值实部和虚部的上下界估计.数值实验表明,与已有的预处理子相比,所构造的预处理子可以使得GMRES迭代方法对网格尺寸,网格形式以及粘度系数的依赖性都比较弱,且在迭代步数和CPU时间上都占优.     

On two particular cases of solving the normal Hankel problem

The normal Hankel problem is one of characterizing all the complex matrices that are normal and Hankel at the same time. The matrix classes that can contain normal Hankel matrices admit a parameterization by real 2 × 2 matrices with determinant one. Here, the normal Hankel problem is solved in the case where the characteristic matrix of a given class is an order two Jordan block for the eigenvalue 1 or ?1.     

How bad are Hankel matrices?

Summary. Considered are Hankel, Vandermonde, and Krylov basis matrices. It is proved that for any real positive definite Hankel matrix of order , its spectral condition number is bounded from below by . Also proved is that the spectral condition number of a Krylov basis matrix is bounded from below by . For , a Vandermonde matrix with arbitrary but pairwise distinct nodes , we show that ; if either or for all , then . Received January 24, 1993/Revised version received July 19, 1993     

A fast method to block-diagonalize a Hankel matrix

In this paper, we consider an approximate block diagonalization algorithm of an n×n real Hankel matrix in which the successive transformation matrices are upper triangular Toeplitz matrices, and propose a new fast approach to compute the factorization in O(n ²) operations. This method consists on using the revised Bini method (Lin et al., Theor Comp Sci 315: 511–523, 2004). To motivate our approach, we also propose an approximate factorization variant of the customary fast method based on Schur complementation adapted to the n×n real Hankel matrix. All algorithms have been implemented in Matlab and numerical results are included to illustrate the effectiveness of our approach.     

Analysis of the Structured Total Least Squares Problem for Hankel/Toeplitz Matrices

The Structured Total Least Squares (STLS) problem is a natural extension of the Total Least Squares (TLS) approach when structured matrices are involved and a similarly structured rank deficient approximation of that matrix is desired. In many of those cases the STLS approach yields a Maximum Likelihood (ML) estimate as opposed to, e.g., TLS.In this paper we analyze the STLS problem for Hankel matrices (the theory can be extended in a straightforward way to Toeplitz matrices, block Hankel and block Toeplitz matrices). Using a particular parametrisation of rank-deficient Hankel matrices, we show that this STLS problem suffers from multiple local minima, the properties of which depend on the parameters of the new parametrisation. The latter observation makes initial estimates an important issue in STLS problems and a new initialization method is proposed. The new initialization method is applied to a speech compression example and the results confirm the improved performance compared to other previously proposed initialization methods.     

Minimal condition number for positive definite Hankel matrices using semidefinite programming

We present a semidefinite programming approach for computing optimally conditioned positive definite Hankel matrices of order n. Unlike previous approaches, our method is guaranteed to find an optimally conditioned positive definite Hankel matrix within any desired tolerance. Since the condition number of such matrices grows exponentially with n, this is a very good test problem for checking the numerical accuracy of semidefinite programming solvers. Our tests show that semidefinite programming solvers using fixed double precision arithmetic are not able to solve problems with n>30. Moreover, the accuracy of the results for 24?n?30 is questionable. In order to accurately compute minimal condition number positive definite Hankel matrices of higher order, we use a Mathematica 6.0 implementation of the SDPHA solver that performs the numerical calculations in arbitrary precision arithmetic. By using this code, we have validated the results obtained by standard codes for n?24, and we have found optimally conditioned positive definite Hankel matrices up to n=100.     

On the skew-symmetric part of the Toeplitz component in the real normal (<Emphasis Type="Italic">T</Emphasis> + <Emphasis Type="Italic">H</Emphasis>)-problem

The real normal Toeplitz-plus-Hankel problem is to characterize the matrices that can be represented as sums of two real matrices of which one is Toeplitz and the other Hankel. For a matrix of this type, relations are found between the skew-symmetric part of the Toeplitz component and the matrix obtained by reversing the order of columns in the Hankel component.     

无界分块算子矩阵的谱分布

本文首先给出次对角元有界的2×2阶无界算子矩阵的Gershgorin定理,然后利用主对角元算子的谱和数值域刻画整个算子矩阵的谱分布.特别地,当次对角元算子互为共轭(反共轭)算子时,结合二次数值域和Gershgorin定理对谱分布给出更精细的描述.     

Block diagonalization and LU-equivalence of Hankel matrices

This article presents a new algorithm for obtaining a block diagonalization of Hankel matrices by means of truncated polynomial divisions, such that every block is a lower Hankel matrix. In fact, the algorithm generates a block LU-factorization of the matrix. Two applications of this algorithm are also presented. By the one hand, this algorithm yields an algebraic proof of Frobenius’ Theorem, which gives the signature of a real regular Hankel matrix by using the signs of its principal leading minors. On the other hand, the close relationship between Hankel matrices and linearly recurrent sequences leads to a comparison with the Berlekamp–Massey algorithm.     

On the reduction of the normal Hankel problem to two particular cases

The normal Hankel problem (NHP) is to describe complex matrices that are normal and Hankel at the same time. The available results related to the NHP can be combined into two groups. On the one hand, there are several known classes of normal Hankel matrices. On the other hand, the matrix classes that may contain normal Hankel matrices not belonging to the known classes were shown to admit a parametrization by real 2 × 2 matrices with determinant 1. We solve the NHP for the cases where the characteristic matrix W of the given class has: (a) complex conjugate eigenvalues; (b) distinct real eigenvalues. To obtain a complete solution of the NHP, it remains to analyze two situations: (1) W is the Jordan block of order two for the eigenvalue 1; (2) W is the Jordan block of order two for ?1.     

Remark on the norm of random Hankel matrices

In recent years, the asymptotic properties of structured random matrices have attracted the attention of many experts involved in probability theory. In particular, R. Adamczak (J. Theor. Probab., Vol. 23, 2010) proved that, under fairly weak conditions, the squared spectral norms of large square Hankel matrices generated by independent identically distributed random variables grow with probability 1, as Nln(N), where N is the size of a matrix. On the basis of these results, by using the technique and ideas of Adamczak’s paper cited above, we prove that, under certain constraints, the squared spectral norms of large rectangular Hankel matrices generated by linear stationary sequences grow almost certainly no faster than Nln(N), where N is the number of different elements in a Hankel matrix. Nekrutkin (Stat. Interface, Vol. 3, 2010) pointed out that this result may be useful for substantiating (by using series of perturbation theory) so-called “signal subspace methods,” which are often used for processing time series. In addition to the main result, the paper contains examples and discusses the sharpness of the obtained inequality.